class_docstr: This class implements a model-specific automatic speech recognizer using the end-to-end speech recognizer
  DeepSpeech and PyTorch. It supports both version 2 and version 3 of DeepSpeech models as released at
  https://github.com/SeanNaren/deepspeech.pytorch.
  
  | Paper link: https://arxiv.org/abs/1512.02595
docstr: Compute the gradient of the loss function w.r.t. `x`.
  
  :param x: Samples of shape (nb_samples, seq_length). Note that, it is allowable that sequences in the batch
            could have different lengths. A possible example of `x` could be:
            `x = np.array([np.array([0.1, 0.2, 0.1, 0.4]), np.array([0.3, 0.1])])`.
  :param y: Target values of shape (nb_samples). Each sample in `y` is a string and it may possess different
            lengths. A possible example of `y` could be: `y = np.array(['SIXTY ONE', 'HELLO'])`.
  :return: Loss gradients of the same shape as `x`.
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code: def loss_gradient(
      self, x: np.ndarray, y: np.ndarray, **kwargs
  ) -> np.ndarray:
      """
      Compute the gradient of the loss function w.r.t. `x`.
  
      :param x: Samples of shape (nb_samples, seq_length). Note that, it is allowable that sequences in the batch
                could have different lengths. A possible example of `x` could be:
                `x = np.array([np.array([0.1, 0.2, 0.1, 0.4]), np.array([0.3, 0.1])])`.
      :param y: Target values of shape (nb_samples). Each sample in `y` is a string and it may possess different
                lengths. A possible example of `y` could be: `y = np.array(['SIXTY ONE', 'HELLO'])`.
      :return: Loss gradients of the same shape as `x`.
      """
      # Apply preprocessing
      x_preprocessed, _ = self._apply_preprocessing(x, None, fit=False)
  
      x_in = np.empty(len(x_preprocessed), dtype=object)
      x_in[:] = list(x_preprocessed)
  
      # Put the model in the training mode, otherwise CUDA can't backpropagate through the model.
      # However, model uses batch norm layers which need to be frozen
      self._model.train()
      self.set_batchnorm(train=False)
  
      # Transform data into the model input space
      (
          inputs,
          targets,
          input_rates,
          target_sizes,
          _,
      ) = self._transform_model_input(
          x=x_in, y=y, compute_gradient=True
      )
  
      # Compute real input sizes
      input_sizes = input_rates.mul_(inputs.size()[-1]).int()
  
      # Call to DeepSpeech model for prediction
      outputs, output_sizes, _ = self._model(
          inputs.to(self._device), input_sizes.to(self._device)
      )
      outputs = outputs.transpose(0, 1)
  
      if self._version == 2:
          outputs = outputs.float()
      else:
          outputs = outputs.log_softmax(-1)
  
      # Compute the loss
      loss = self.criterion(
          outputs, targets, output_sizes, target_sizes
      ).to(self._device)
  
      # Average the loss by mini batch if version 2 of DeepSpeech is used
      if self._version == 2:
          loss = loss / inputs.size(0)
  
      # Compute gradients
      if self.use_amp:  # pragma: no cover
          from apex import amp  # pylint: disable=E0611
  
          with amp.scale_loss(loss, self.optimizer) as scaled_loss:
              scaled_loss.backward()
  
      else:
          loss.backward()
  
      # Get results
      results_list = []
      for i, _ in enumerate(x_in):
          results_list.append(x_in[i].grad.cpu().numpy().copy())
  
      results = np.array(results_list)
  
      if results.shape[0] == 1:
          results_ = np.empty(len(results), dtype=object)
          results_[:] = list(results)
          results = results_
  
      results = self._apply_preprocessing_gradient(x, results)
  
      if x.dtype != object:
          results = np.array(
              [i for i in results], dtype=x.dtype
          )  # pylint: disable=R1721
          assert results.shape == x.shape and results.dtype == x.dtype
  
      # Unfreeze batch norm layers again
      self.set_batchnorm(train=True)
  
      return results
  
imports: import logging
  from typing import TYPE_CHECKING, List, Optional, Tuple, Union
  from pkg_resources import packaging
  import numpy
  from art import config
  from art.estimators.pytorch import PyTorchEstimator
  from art.estimators.speech_recognition.speech_recognizer import SpeechRecognizerMixin, PytorchSpeechRecognizerMixin
  from art.utils import get_file
label_desc: Use image node_id 3 for calling the PyTorchDeepSpeech obj's underlying member method code with example usage: obj.loss_gradient(x, y) and returns: results